Image-Text-to-Text
GGUF
English
multilingual
llama.cpp
rocmfpx
gfx1151
strix-halo
qwen35moe
Mixture of Experts
rocm
amdgpu
ROCmFP4
imatrix
conversational
Instructions to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16 # Run inference directly in the terminal: llama cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16 # Run inference directly in the terminal: llama cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16 # Run inference directly in the terminal: ./llama-cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Use Docker
docker model run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
- LM Studio
- Jan
- vLLM
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
- Ollama
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Ollama:
ollama run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
- Unsloth Desktop
- Pi
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Docker Model Runner:
docker model run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
- Lemonade
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Run and chat with the model
lemonade run user.Ornith-1.5-35B-ROCmFP4-STRIX_LEAN-BF16
List all available models
lemonade list
- Hermes Agent
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload NOTICE with huggingface_hub
Browse files
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Ornith-1.5-35B-ROCmFP4-STRIX_LEAN
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Copyright 2026 pugant
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This is a derivative work. Original model and its license are preserved.
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DERIVATION CHAIN:
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Qwen3.5-VL-MoE (base architecture)
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└── ornith-ai/Ornith-1.5-35B (A3B) (MIT License, Copyright DeepReinforce Team)
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└── this GGUF quantization by pugant
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QUANTIZATION:
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Preset: Q4_0_ROCMFP4_STRIX_LEAN (type 106, ~4.29 BPW)
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Tool: charlie12345/ROCmFPX fork of llama.cpp (MIT)
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https://github.com/charlie12345/ROCmFPX
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Source BF16 GGUF: ornith-ai/Ornith-1.5-35B-A3B-GGUF (Ornith-1.5-35B-BF16.gguf, MIT)
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imatrix: bartowski (precomputed on 573 chunks, calibration-v6,
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included as imatrix-Ornith-1.5-35B-bartowski.gguf with attribution)
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https://huggingface.co/bartowski/Ornith-1.5-35B-A3B-GGUF
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RUNTIME:
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Requires kyuz0/amd-strix-halo-toolboxes + charlie12345/ROCmFPX
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https://github.com/kyuz0/amd-strix-halo-toolboxes
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